fuel nozzle presentation fit conference

8/3/2019 Fuel Nozzle Presentation Fit Conference

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FUEL

NOZZLE

SPRAY

P ATTERN CLASSIFIER

By: Mubeen Ghafoor



OUTLINE

Scope of this Study

Introduction

Traditional Fuel Nozzle Tester

Drawbacks in Manual Testing Systems

Proposed Fuel Nozzle Testing System

Flow Diagram of Fuel Nozzle Spray Pattern Classifier System

ROI (Region of Interest) Extraction

Spray Pattern Angle Calculation

Analyzing Streaks and Voids in Spray Pattern

y Enhancement

y Feature Extraction

Conclusion

2



SCOPE OF THIS STUDY

Image Processing field is expanding many folds due to cheaper

imaging technologies and higher computational powers available

now a days.

Therefore Vision based applications are becoming much more

feasible and reliable.

In this study an industrial problem of classification of faulty Fuel Nozzles is considered and a solution is

proposed by analyzing their spray pattern through vision

based algorithms.

3



INTRODUCTION

Fuel Nozzle is a critical component in performance of any dieselengine, especially in aircraft engines.

In this study, Fuel nozzles of C-130 Hercules engine, are used.

C-130 Hercules engine is a diesel engine with six fuel nozzlesspraying fuel in combustion engine.

If one fuel nozzle gets faulty the combustion engine can burn andmay cause engine to cease during the flight.

So it becomes obligatory to avoid faulty fuel nozzle at all costs.

Therefore fuel nozzle testing is one of the most essentialprocedures involved in the maintenance of the aircraft engine. 4



TRADITION A L FUEL NOZZLE TESTER

Nozzle testers are designed so that the spray pattern of the nozzle can be

examined.

A Fuel nozzle is classified as faulty if,

Fuel flow is non-uniform at any

part of the spray pattern.

Streaks and voids are present in

its spray pattern.

Angles of spray pattern are not

within specified range at a specific

pressure.5



TRADITION A L FUEL NOZZLE TESTER

Currently, the spray pattern is analyzed manually by the operator.

The angle is calculated by lookingat the spray pattern and then

looking at its impact on arced scale.

Similarly, spray pattern is checked

for any non-uniformity or streaks

and voids in the fuel flow region.

Frontal View of Fuel Nozzle Tester

6



SPRAY P A TTERN OF FUEL NOZZLE

7



DRAWBACK S IN M A NU A L TESTING

S Y STEMS

As spray angles and streaks and voids are measured manually by

the operator.

Therefore, this process may involves human error.

Manual testing is time consuming.

Operator has to manually record all the results corresponding to

each test.

Finding previous service history of any fuel nozzles is

cumbersome.

8



MOTI VA TION

So there is a need for a computerized system, which can observe spray

pattern of fuel nozzle from the glass chamber and can classify whether

fuel nozzle is functional (working properly) or it is defective.

9



PROPOSED FUEL NOZZLE TESTING

S Y STEM

The Proposed Fuel Nozzle Spray Pattern System is composed of a

Computer and a camera.

The Computer continuously acquires data from the camera during

a test. The operator determines when the proper test conditions are met,

and captures the image for analysis and storage.

10



PROPOSED FUNCTION A LIT Y

The proposed system can perform the following functions

automatically and accurately:

Calculate angles formed by spray pattern at different pressures.

Find discontinuities, non-uniformities, streaks and voids in spray

pattern.

Classify whether fuel nozzle is functional (working properly) or it

is defective based on above two functionalities.

Save results of fuel nozzles in database.

11



PROPOSED FLOW DI AGRAM OF FUEL

NOZZLE SPRAY P A TTERN CL A SSIFIER

Grey Scale

Conversion

Find Angles Subtended

by Spray Pattern at

specific pressure

Streaks and Voids

Detection in Spray

Pattern

Fuel Nozzle

Serviceability

Status

ROI Extraction

Input Image

Acceptability

Criteria

12



ROI (REGION OF INTEREST) EXTRACTION

The intensity values of image throughout the fuel flow region is almost same, and it

is different from background

The fuel flow region is bounded between the two external lines of Spray pattern.

To extract these lines :

Firstly, edges are extracted using Canny edge detector.

Then Hough transform for straight lines is calculated.

To extract fuel flow region, only two most distinctive lines are selected, which gives

external lines of fuel flow region.

For ROI extraction region between these two lines is cropped from original image.

Hough transform also gives Spray Angles of these two lines.

13



A NALYZING STREAKS AND V OIDS IN SPRAYP ATTERN

Streaks Voids

Status

Enhancement

Apply

Gabor

Filtering

Feature

Extraction

Euclidean

Distance

ClassifierInput

Image

14

Flow Diagram for Analyzing Streaks and Voids in Spray Pattern



ENH A NCEMENT

Enhanced

Image

Log of

Image

Fourier

Transform

High

Frequency

Emphasis

Filtering

InverseFourier

Transform

Exponential

of Image

Input

ImageThe non-uniform spread of light causes non-uniform

illumination over the fuel flow image so some parts of

flow appear darker as compared to outer parts of flow

which requires illumination adjustment.

This discrepancy can be catered by using Homomorphicfiltering.

15



FE A TURE EXTRACTION

The central part of the feature extraction lies within the Gabor filtering

stage. The purpose of the filtering stage is to enhance the streak and

voids structure while reducing noise in the image.

The real part of 2-D Gabor function is given by a cosine wave modulatedby a Gaussian is given as.

)2cos(.2

1exp),:,(

2

2

2

2

U

U UT

WW U fx

y x f y x g

y x ¼¼

½

»

¬¬

-

«

¹¹

º

¸

©©

ª

¨!

x= x cos + y sin ,

y = - x sin + y cos .

Where f is the frequency of cosine wave, is the orientation

of filter, x and y are the standard deviations of the

Gaussian envelope along the x -axes and y-axes, respectively

16



FE A TURE EXTRACTION

Gabor filters of different frequencies and directions are applied on the

enhanced fuel flow image.

For each filtered variance of the output image within ROI is computed.

Mean of these variances is estimated and saved as a feature for that nozzle.

Figure shows application of Gabor filter on enhanced image of faulty and functional fuel nozzles.

17



TRA INING

We have trained our classifier by taking 50 functional

(properly working) fuel nozzles and 50 defective fuel

nozzles.

So there are in total 100 features for a specific pressure.

18



TESTING

For classifying a test fuel nozzle these steps are followed:

ROI of test fuel nozzle is extracted

Spray Angle are estimated

Fuel Flow Pattern is enhanced by Homomorphic filtering

Enhanced image is convolved by Gabor filter bank and feature for

test nozzle is calculated

This test feature is then compared with features of training set

using Euclidean distance classifier19



TESTING

Test fuel nozzle is classified as functional if;

y Spray Angles are within range for a specific pressure,

y

Euclidean distance between features of functional fuel nozzles and featureof test fuel nozzle is smaller as compared to Euclidean distance between

features of defective fuel nozzles and feature of test fuel nozzle.

20



CONCLUSION

A reliable, time efficient and cost effective solution based on

vision test systems is proposed for the ease of operators and

quality control engineers.

Automated image analysis measurements greatly reduce errorsthat are introduced by operator when using manual measurement

techniques.

Using this system, the process time for image acquisition, image

analysis, and saving results in database is very less as compared

to manual system.

21



Q & A

22

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